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    Developing Complete Conditional Probability Tables from Fractional Data for Bayesian Belief Networks

    Source: Journal of Computing in Civil Engineering:;2007:;Volume ( 021 ):;issue: 004
    Author:
    Zhong Tang
    ,
    Brenda McCabe
    DOI: 10.1061/(ASCE)0887-3801(2007)21:4(265)
    Publisher: American Society of Civil Engineers
    Abstract: A Bayesian belief network (BBN) can be a powerful tool in decision making processes. Development of a BBN requires data or expert knowledge to assist in determining the structure and probabilistic parameters in the model. As data are seldom available in the engineering decision making domain, a major barrier in using domain experts is that they are often required to supply a huge and intractable number of probabilities. Techniques for using fractional data to develop complete conditional probability tables were examined. The results showed good predictability of the missing data in a linear domain by the piecewise representation method. By using piecewise representation, the number of probabilities to be elicited for a binary child node with
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      Developing Complete Conditional Probability Tables from Fractional Data for Bayesian Belief Networks

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    http://yetl.yabesh.ir/yetl1/handle/yetl/43326
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    contributor authorZhong Tang
    contributor authorBrenda McCabe
    date accessioned2017-05-08T21:13:21Z
    date available2017-05-08T21:13:21Z
    date copyrightJuly 2007
    date issued2007
    identifier other%28asce%290887-3801%282007%2921%3A4%28265%29.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/43326
    description abstractA Bayesian belief network (BBN) can be a powerful tool in decision making processes. Development of a BBN requires data or expert knowledge to assist in determining the structure and probabilistic parameters in the model. As data are seldom available in the engineering decision making domain, a major barrier in using domain experts is that they are often required to supply a huge and intractable number of probabilities. Techniques for using fractional data to develop complete conditional probability tables were examined. The results showed good predictability of the missing data in a linear domain by the piecewise representation method. By using piecewise representation, the number of probabilities to be elicited for a binary child node with
    publisherAmerican Society of Civil Engineers
    titleDeveloping Complete Conditional Probability Tables from Fractional Data for Bayesian Belief Networks
    typeJournal Paper
    journal volume21
    journal issue4
    journal titleJournal of Computing in Civil Engineering
    identifier doi10.1061/(ASCE)0887-3801(2007)21:4(265)
    treeJournal of Computing in Civil Engineering:;2007:;Volume ( 021 ):;issue: 004
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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